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There's a lot of confusion about Hadoop and where it fits into the overall Big Data landscape.

There's a lot of confusion about Hadoop and where it fits into the overall Big Data landscape. Datamation takes a look at this open source software framework that stores and analyzes large data sets distributed across multiple servers--and dispels six big Hadoop myths.

Myth #1: Hadoop is a database

Hadoop is often talked about like it's a database, but it isn't. "There’s nothing in the core Hadoop platform like a query or an index," said Marshall Bockrath-Vandegrift, a software engineer with Damballa, a security company. Damballa uses Hadoop to analyze real-time security threats.

Myth #2: Hadoop Will Require Programmers

Depending on what you plan to do, this myth may come true. If you plan to build the next great Hadoop-based Big Data suite, you'll need programmers who can write in Java and understand specialized MapReduce programming. However, if you're content to build on the work of others, programming shouldn't scare you off. Most data integration tools will have GUIs that abstract MapReduce programming complexity, and many come with pre-built templates.

Myth #3: Using Hadoop is Cheap

This is a common misconception associated with anything open source. Just because you're able to reduce or eliminate the initial costs of purchasing software doesn't mean you'll necessarily save money. One of the problems with the cloud, for instance, is that it's so easy to run a science project on Amazon that developers of all sorts throw projects up in AWS, forget about them, but keep paying for them.